| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.289 | | leniency | 0.579 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.02% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1113 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 50.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1113 | | totalAiIsms | 11 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | word | "the last thing" | | count | 1 |
|
| | highlights | | 0 | "perfect" | | 1 | "traced" | | 2 | "etched" | | 3 | "flickered" | | 4 | "stomach" | | 5 | "echoed" | | 6 | "familiar" | | 7 | "shimmered" | | 8 | "pulsed" | | 9 | "the last thing" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "stomach dropped/sank" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 106 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 106 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 133 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 5 | | totalWords | 1104 | | ratio | 0.005 | | matches | | 0 | "tie clip" | | 1 | "rift" | | 2 | "interfering" | | 3 | "couldn’t" |
| |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 62.94% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 804 | | uniqueNames | 20 | | maxNameDensity | 1.74 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Davies" | | discoveredNames | | Quinn | 1 | | Tube | 1 | | Constable | 1 | | Tom | 1 | | Davies | 10 | | Veil | 1 | | Market | 1 | | Harlow | 14 | | Langley | 2 | | Bank | 1 | | City | 1 | | Morris | 1 | | Bristol | 1 | | Greek | 2 | | Met | 1 | | Eva | 4 | | Kowalski | 1 | | Internal | 1 | | Affairs | 1 | | God | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Constable" | | 2 | "Tom" | | 3 | "Davies" | | 4 | "Market" | | 5 | "Harlow" | | 6 | "Morris" | | 7 | "Eva" | | 8 | "Kowalski" | | 9 | "Affairs" |
| | places | | 0 | "Langley" | | 1 | "Bank" | | 2 | "Bristol" | | 3 | "Met" |
| | globalScore | 0.629 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1104 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 133 | | matches | | 0 | "knew that sound" | | 1 | "screamed that she" |
| |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 22.08 | | std | 18.52 | | cv | 0.839 | | sampleLengths | | 0 | 56 | | 1 | 32 | | 2 | 73 | | 3 | 49 | | 4 | 7 | | 5 | 8 | | 6 | 52 | | 7 | 23 | | 8 | 11 | | 9 | 33 | | 10 | 6 | | 11 | 16 | | 12 | 57 | | 13 | 11 | | 14 | 12 | | 15 | 38 | | 16 | 24 | | 17 | 7 | | 18 | 10 | | 19 | 2 | | 20 | 2 | | 21 | 57 | | 22 | 8 | | 23 | 19 | | 24 | 14 | | 25 | 36 | | 26 | 40 | | 27 | 40 | | 28 | 2 | | 29 | 54 | | 30 | 22 | | 31 | 12 | | 32 | 1 | | 33 | 12 | | 34 | 5 | | 35 | 35 | | 36 | 21 | | 37 | 9 | | 38 | 4 | | 39 | 28 | | 40 | 14 | | 41 | 1 | | 42 | 26 | | 43 | 3 | | 44 | 39 | | 45 | 13 | | 46 | 42 | | 47 | 3 | | 48 | 10 | | 49 | 5 |
| |
| 95.33% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 106 | | matches | | 0 | "been bagged" | | 1 | "was gone" | | 2 | "was involved" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 151 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 1 | | flaggedSentences | 8 | | totalSentences | 133 | | ratio | 0.06 | | matches | | 0 | "The air smelled wrong—like burnt copper and wet newspaper, not the usual underground damp of abandoned Tube stations." | | 1 | "Not body heat—something older, something that hummed against her fingertips like a live wire." | | 2 | "The grainy CCTV showed Langley boarding the train at Bank, briefcase in hand, tie slightly askew—classic City boy after a long day." | | 3 | "He took a seat by the door, glanced at his watch, then—" | | 4 | "One frame he existed; the next he didn’t." | | 5 | "Then, beneath it, the faintest chime—like a shop bell ringing in reverse." | | 6 | "The tiles here were wrong—too clean, the grout too white." | | 7 | "Because if Eva Kowalski was involved in this—and Harlow’s instincts screamed that she was—then the last thing she needed was Internal Affairs breathing down her neck before she’d had a chance to separate the lies from the truth." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 810 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.037037037037037035 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.012345679012345678 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 133 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 133 | | mean | 8.3 | | std | 7.66 | | cv | 0.922 | | sampleLengths | | 0 | 17 | | 1 | 8 | | 2 | 18 | | 3 | 13 | | 4 | 17 | | 5 | 15 | | 6 | 2 | | 7 | 17 | | 8 | 12 | | 9 | 2 | | 10 | 4 | | 11 | 23 | | 12 | 8 | | 13 | 5 | | 14 | 11 | | 15 | 32 | | 16 | 6 | | 17 | 3 | | 18 | 4 | | 19 | 2 | | 20 | 6 | | 21 | 19 | | 22 | 8 | | 23 | 21 | | 24 | 4 | | 25 | 5 | | 26 | 18 | | 27 | 5 | | 28 | 6 | | 29 | 4 | | 30 | 14 | | 31 | 9 | | 32 | 4 | | 33 | 2 | | 34 | 6 | | 35 | 2 | | 36 | 14 | | 37 | 9 | | 38 | 48 | | 39 | 6 | | 40 | 2 | | 41 | 3 | | 42 | 10 | | 43 | 2 | | 44 | 22 | | 45 | 4 | | 46 | 12 | | 47 | 3 | | 48 | 7 | | 49 | 2 |
| |
| 67.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.43609022556390975 | | totalSentences | 133 | | uniqueOpeners | 58 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 85 | | matches | | 0 | "Just a perfect human silhouette," | | 1 | "Just for a second." | | 2 | "Then, beneath it, the faintest" |
| | ratio | 0.035 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 85 | | matches | | 0 | "She exhaled through her nose," | | 1 | "She crouched, running her fingers" | | 2 | "He snapped his fingers" | | 3 | "He led her to the" | | 4 | "She reached for it." | | 5 | "She yanked her arm free." | | 6 | "She tossed the token back" | | 7 | "He pulled out his phone," | | 8 | "He took a seat by" | | 9 | "She knew that sound." | | 10 | "His smirk faltered." | | 11 | "He made air quotes" | | 12 | "She stormed toward the platform" | | 13 | "Her boot heel caught on" | | 14 | "It flipped up, revealing a" | | 15 | "She stood abruptly, brushing concrete" | | 16 | "she said, already moving toward" | | 17 | "It wasn’t a question." | | 18 | "She fixed him with a" | | 19 | "She turned left." |
| | ratio | 0.235 | |
| 71.76% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 66 | | totalSentences | 85 | | matches | | 0 | "The brass compass needle twitched" | | 1 | "Harlow Quinn’s fingers tightened around" | | 2 | "The air smelled wrong—like burnt" | | 3 | "She exhaled through her nose," | | 4 | "Detective Constable Tom Davies adjusted" | | 5 | "The Veil Market had moved" | | 6 | "Harlow stepped over the chalk" | | 7 | "She crouched, running her fingers" | | 8 | "The chalk came away damp." | | 9 | "Davies said, flipping through his" | | 10 | "He snapped his fingers" | | 11 | "Harlow stood abruptly." | | 12 | "He led her to the" | | 13 | "Harlow didn’t need to ask" | | 14 | "The bone token’s jagged edges" | | 15 | "She reached for it." | | 16 | "Davies caught her wrist" | | 17 | "She yanked her arm free." | | 18 | "The token was warm." | | 19 | "She tossed the token back" |
| | ratio | 0.776 | |
| 58.82% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 85 | | matches | | 0 | "Because if Eva Kowalski was" |
| | ratio | 0.012 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 5 | | matches | | 0 | "Just a perfect human silhouette, as if someone had pressed a stencil against the grimy tiles and traced the absence of a man." | | 1 | "The bone token’s jagged edges pressed against the evidence bag, its surface etched with the same sigils that marked her compass." | | 2 | "Not body heat—something older, something that hummed against her fingertips like a live wire." | | 3 | "She fixed him with a look that made younger officers flinch." | | 4 | "Right led deeper into the abandoned network, toward the older lines, the ones that didn’t appear on any official map." |
| |
| 79.55% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 1 | | matches | | 0 | "Detective Constable Tom Davies adjusted, his torch beam jittering across the platform" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 38 | | tagDensity | 0.158 | | leniency | 0.316 | | rawRatio | 0.167 | | effectiveRatio | 0.053 | |